Use the data in LOANAPP for this exercise. (i) How many...

Use the data in LOANAPP for this exercise. (i) How many observations have $o b r a t>40,$ that is, other debt obligations more than 40$\%$ of total income? (ii) Reestimate the model in part (iii) of Computer Exercise $\mathrm{C8}$ , excluding observations with obrat $>40 .$ What happens to the estimate and $t$ statistic on white? (iii) Does it appear that the estimate of $\beta_{\text {white}}$ is overly sensitive to the sample used?

Use the data in LOANAPP for this exercise.
(i) How many observations have $o b r a t>40,$ that is, other debt obligations more than 40$\%$ of total income?
(ii) Reestimate the model in part (iii) of Computer Exercise $\mathrm{C8}$ , excluding observations with obrat $>40 .$ What happens to the estimate and $t$ statistic on white?
(iii) Does it appear that the estimate of $\beta_{\text {white}}$ is overly sensitive to the sample used?

Key Concepts

Regression Re-estimation

Regression re-estimation refers to the process of re-running a regression analysis after altering the sample or modifying the model. In the context of this question, after excluding observations with obrat greater than 40%, the regression model is reestimated to examine changes in coefficient estimates and inferential statistics, which sheds light on the model's sensitivity to sample composition.

Statistical Inference and t-Statistics

t-Statistics are used in regression analysis to determine the statistical significance of individual coefficients. Their value indicates whether a variable has a significant effect on the dependent variable. By comparing t-statistics before and after the exclusion of certain observations, one can assess the reliability and robustness of the estimated coefficients and draw conclusions about potential sample sensitivity.

Sensitivity Analysis

Sensitivity analysis in econometrics involves determining how the estimates of model parameters change in response to modifications in the sample or model specification. Here, the focus is on examining the estimate of the beta coefficient on the white variable to determine if its value and significance is overly influenced by the presence of the excluded observations.

Sample Selection and Outlier Removal

This concept involves deliberately excluding certain observations that may be deemed as outliers or atypical based on a specific criterion—in this case, observations where other debt obligations exceed 40% of total income. Removing such observations can help in understanding the behavior of the model under a 'cleaner' set of data and also in assessing the robustness of the model's results.

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